Spectral Analysis of Molecular Features: When Richer Features Do Not Guarantee Better Generalization
arXiv:2510.14217v2 Announce Type: replace Abstract: The spectral properties of feature embeddings offer critical insights into model generalization and representation quality. While deep learning models are widely used for molecular property prediction, kernel methods remain competitive in low-data regimes, yet their spectral behavior is largely unexplored. We present the first comprehensive spectral analysis of kernel ridge regression across diverse representations-including molecular fingerprints (ECFP), pretrained transformers, graph neural networks, and 3D descriptors-evaluated on QM9 and
The proliferation of deep learning models in molecular science highlights the ongoing need for rigorous analysis of their underlying mechanisms and limitations, especially concerning generalization in complex domains.
Understanding the spectral properties of molecular feature embeddings can lead to more robust and generalizable AI models for drug discovery and materials science, impacting a vast array of industries.
This research provides a deeper theoretical understanding of model generalization in a critical scientific domain, potentially guiding the development of more effective machine learning approaches beyond simple feature richness.
- · AI researchers in chemistry and materials science
- · Pharmaceutical R&D
- · Biotechnology companies
- · Computational chemistry platforms
- · Developers relying solely on brute-force feature engineering
- · Organizations without strong theoretical ML capabilities
Improved design principles for AI models in molecular property prediction.
Accelerated discovery of novel molecules with desired properties, impacting drug development and material science.
Enhanced automation and reliability in certain aspects of synthetic biology and chemical engineering, reducing experimental cycles.
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Read at arXiv cs.LG